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Record W1981552817 · doi:10.1002/hec.1012

Revisiting physicians' financial incentives in Quebec: a panel system approach

2005· article· en· W1981552817 on OpenAlexaboutno aff
Abdelhak Nassiri, Lise Rochaix

Bibliographic record

VenueHealth Economics · 2005
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsnot available
Fundersnot available
KeywordsParallelsIncentiveComplementarity (molecular biology)Primary careEnforcementPanel dataPublic economicsEconomicsActuarial scienceMedicineMicroeconomicsOperations managementEconometricsFamily medicinePolitical scienceLaw

Abstract

fetched live from OpenAlex

Do Primary Care Physicians (PCPs) react strategically to financial incentives and if so how? To address this question, we follow a quasi-natural experiment in Quebec, using a panel system technique. In so doing, we both correct for underestimation biases in earlier time series findings and generate new results on the issue of complementarity/substitution between consultations with varying levels of technicality. Under both techniques, we show that PCPs are sensitive to the enforcement and subsequent temporary removals of expenditure caps and more generally, to changes in consultations' relative prices over time. These results support the existence of a discretionary power over the choice of consultation, PCPs increasing strategically the number of the more technical (and therefore more lucrative) consultations when pressed to defend their income. This finding for primary care parallels the now well-established DRG creep in hospitals. The panel system approach offers a better account of the complexity surrounding PCPs' decision-making process. In particular, it successfully addresses issues of physician heterogeneity, jointness between consultations and temporal breaks and generates robust estimates of PCPs volume and quality reactions to regulatory changes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.890
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.068
GPT teacher head0.267
Teacher spread0.198 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations31
Published2005
Admission routes1
Has abstractyes

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